Sentiment Analysis on the Failure of the Indonesian National Team to the 2026 World Cup During Patrick Kluivert's Coaching Period using the Support Vector Machine (SVM) Algorithm

Abstract

This study aims to analyze public sentiment regarding the failure of the Indonesian National Team to qualify for the 2026 FIFA World Cup during Patrick Kluivert’s coaching period using the Support Vector Machine (SVM) algorithm. Data were collected through web scraping from Twitter (X), YouTube, and Detik.com, resulting in 5,060 comments. The collected data were processed using Natural Language Processing (NLP), including case folding, cleaning, tokenization, stopword removal, normalization, and stemming. The labeled data were transformed using the Term Frequency–Inverse Document Frequency (TF-IDF) method and divided into training and testing sets with an 80:20 ratio. The classification model was developed using a linear kernel SVM and implemented through a Streamlit-based web application for interactive sentiment prediction. The results showed that negative sentiment dominated with 55.0%, followed by positive sentiment at 36.4% and neutral sentiment at 8.6%. Model evaluation achieved an accuracy of 78.44%, precision of 78.54%, recall of 78.44%, and f1-score of 78.48%. These findings indicate that the SVM method is effective in classifying public sentiment toward the performance of the Indonesian National Team.